Quantifying Missed Opportunities for Cumulative Forest Road Carbon Storage over the Past 50 Years in the Boreal Forest of Eastern Canada
Bibliographic record
Abstract
Forest road networks are essential for forest operations but significantly contribute to carbon loss and landscape fragmentation in boreal ecosystems. This study evaluates the potential of reforesting unused forest roads to enhance carbon storage (CS) in Quebec’s boreal forests. Four reforestation scenarios were simulated using spatial data from AQréseau+ and the Ecoforestry Map of Quebec, combined with the CBM-CFS3 carbon model. These scenarios varied in site preparation conditions and species selection, including the use of fast-growing local species. Random forest (RF) models were applied to analyze the influence of key variables on CS dynamics, focusing on the road area and years to harvest. The study area covered approximately 294,000 km2, and the temporal dimension was incorporated by estimating the construction dates of forest roads. Results show that scenarios integrating soil preparation and fast-growing species (S1I1) achieved the highest CS potential, with up to 6.8 million tons (Mt) of additional carbon stored over a 40–100 year period for medium-category roads, compared to 1.15 million tons in scenarios without intervention (S0I0). These findings underscore the role of reforestation in enhancing CS within managed forests. Future work should prioritize road segments for reforestation, considering ecological benefits, operational feasibility, and climate resilience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".